The Relative Ability of Earnings and Cash Flow Data in Forecasting Future Cash Flows: Some Australian Evidence
Bibliographic record
Abstract
Purpose: This paper examines the relative predictive ability of earnings, cash flow from operations as reported in the cash flow statement, and two traditional measures of cash flows (i.e. earnings plus depreciation and amortisation expense, and working capital from operations) in forecasting future cash flows for Australian companies. Further, an empirical investigation of the extent to which firm size, as a contextual factor, influences the predictability of earnings and cash flow from operations is presented.\nMethodology: Our sample includes 323 companies listed on the Australian Stock Exchange between 1992 and 2004 (3,512 firm-years). We employ the ordinary least squares and fixed effects approaches to estimate our regression models. To evaluate the forecasting performance of the regression models, both within-sample and out-of-sample forecasting tests are employed.\nFindings: We provide evidence that reported cash flow from operations has more power in predicting future cash flows than earnings and traditional cash flow measures. Further, the predictability of both earnings and cash flow from operations significantly increases with firm size. However, the superiority of cash flow from operations to earnings in predicting future cash\nflows is robust across small, medium and large firms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".